This research work was designed to evaluate the suitability and applicability of hyperspectral radiometry technology for robustly detecting adulterants in diary milk. The most common milk adulterants are (a) soda, (b) urea, (c) water and (d) detergents. The main contribution of this paper is to build a mathematical model to enable quantifying the degree of common adulterants present in milk. Data was collected using a portable spectroradiometer (Eko MS-720) which measures the spectral irradiance in the range from visible to near-infrared irradiance (350 nm 1050 nm) using samples of milk contaminated with four different adulterants (soda, urea, water and detergent) with known degree of contamination deliberately added in milk. In this study, we used the data in the range of 350 - 1050 nm to identify spectral signatures of different adulterants with different degree of concentration. Data cleansing, in the form of pre-processing was followed by machine learning techniques to create a model to capture the adulterants and also the degree of adulteration. Linear regression along with wrapper subset eval as attribute evaluator and best first search as search option was found to create the best model. Root Mean Square Error (RMSE) and Correlation Coefficient (CC) metrics were used to select the best model. The best model for detecting the degree of adulteration due to soda, urea, water and detergent in milk was found to have an RMSE of 0.027, 0.0069, 0.0382 and 0.0281 respectively while CC was 0.9919, 0.9997, 0.9887 and 0.9938 respectively. The preliminary experimental results demonstrate the effective use of spectroradiometer and machine learning technique in reliably detecting adulterants in milk.